SAR and QSAR modeling of endocrine disruptors
暂无分享,去创建一个
J. Devillers | J. Devillers | N. Marchand-Geneste | J. Porcher | N. Marchand-Geneste | A. Carpy | J. M. Porcher | A. Carpy | J. Devillers | A. Carpy
[1] R. Cramer,et al. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. , 1988, Journal of the American Chemical Society.
[2] Richard D Beger,et al. The use of carbon thirteen nuclear magnetic resonance spectra to predict dioxin and furan binding affinities to the aryl hydrocarbon receptor , 2003, Environmental toxicology and chemistry.
[3] Arja Asikainen,et al. Spectroscopic QSAR Methods and Self-Organizing Molecular Field Analysis for Relating Molecular Structure and Estrogenic Activity , 2003, J. Chem. Inf. Comput. Sci..
[4] J. Devillers,et al. A General QSAR Model for Predicting the Acute Toxicity of Pesticides to Oncorhynchus mykiss , 2000, SAR and QSAR in environmental research.
[5] Traian Sulea,et al. Dioxin-Type Activity for Polyhalogenated Arylic Derivatives. A QSAR Model Based on MTD-Method , 1995 .
[6] J. Ruuskanen,et al. Performance of (consensus) kNN QSAR for predicting estrogenic activity in a large diverse set of organic compounds , 2004, SAR and QSAR in environmental research.
[7] Gilles Klopman,et al. Structure-activity relationship study of a diverse set of estrogen receptor ligands (I) using MultiCASE expert system. , 2003, Chemosphere.
[8] Tudor I. Oprea,et al. Ligand-based identification of environmental estrogens. , 1996, Chemical research in toxicology.
[9] R Serafimova,et al. Androgen receptor binding affinity of pesticide "active" formulation ingredients. QSAR evaluation by COREPA method , 2002, SAR and QSAR in environmental research.
[10] E. Dodds,et al. Molecular structure in relation to oestrogenic activity. Compounds without a phenanthrene nucleus , 1938 .
[11] Weida Tong,et al. Influence of the structural diversity of data sets on the statistical quality of three-dimensional quantitative structure-activity relationship (3D-QSAR) models: predicting the estrogenic activity of xenoestrogens. , 2002, Chemical research in toxicology.
[12] J P Raynaud,et al. Multivariate analysis by the minimum spanning tree method of the structural determinants of diphenylethylenes and triphenylacrylonitriles implicated in estrogen receptor binding, protein kinase C activity, and MCF7 cell proliferation. , 1992, Journal of medicinal chemistry.
[13] J P Raynaud,et al. Correspondence analysis applied to steroid receptor binding. , 1986, Journal of medicinal chemistry.
[14] Lee G. Pedersen,et al. PCB and Related Compound Binding to the Ah Receptor(s) Theoretical Model Based on Molecular Parameters and Molecular Mechanics , 1985 .
[15] R. Robinson,et al. Œstrogenic Activity of Alkylated Stilbœstrols , 1938, Nature.
[16] J. Devillers,et al. Strengths and Weaknesses of the Backpropagation Neural Network in QSAR and QSPR Studies , 1996 .
[17] H S Rosenkranz,et al. Applications of the case/multicase SAR method to environmental and public health situations. , 1999, SAR and QSAR in environmental research.
[18] D. Minor,et al. Using three-dimensional quantitative structure-activity relationships to examine estrogen receptor binding affinities of polychlorinated hydroxybiphenyls. , 1995, Environmental health perspectives.
[19] L. Gray,et al. Three-dimensional quantitative structure--activity relationships for androgen receptor ligands. , 1996, Toxicology and applied pharmacology.
[20] James Devillers. A NEURAL NETWORK SAR MODEL FOR ALLERGIC CONTACT DERMATITIS , 2000 .
[21] H. Tunaz. Insect Growth Regulators for Insect Pest Control , 2004 .
[22] Antti Poso,et al. Binding of some dioxins and dibenzofurans to the Ah receptor. A QSAR model based on comparative molecular field analysis (CoMFA) , 1993 .
[23] Weida Tong,et al. QSARs for Endocrine Disruption Priority Setting Database 2: The Integrated 4‐Phase Model , 2003 .
[24] H. Takigami,et al. Structural requirements for the interaction of 91 hydroxylated polychlorinated biphenyls with estrogen and thyroid hormone receptors. , 2005, Toxicological sciences : an official journal of the Society of Toxicology.
[25] Dan A. Buzatu,et al. Combining NMR spectral and structural data to form models of polychlorinated dibenzodioxins, dibenzofurans, and biphenyls binding to the AhR , 2002, J. Comput. Aided Mol. Des..
[26] Steven P. van Helden,et al. Quantitative Structure-Activity Relationship Studies of Progesterone Receptor Binding Steroids , 2000, J. Chem. Inf. Comput. Sci..
[27] K. Tuppurainen. EEVA (Electronic Eigenvalue): A New QSAR/QSPR Descriptor for Electronic Substituent Effects Based on Molecular Orbital Energies , 1999 .
[28] Gerald T. Ankley,et al. New developments in a hazard identification algorithm for hormone receptor ligands , 1999 .
[29] Gerald T. Ankley,et al. The role of ligand flexibility in predicting biological activity: Structure–activity relationships for aryl hydrocarbon, estrogen, and androgen receptor binding affinity , 1998 .
[30] K. Chae,et al. Estrogen receptor-binding activity of polychlorinated hydroxybiphenyls: conformationally restricted structural probes. , 1988, Molecular pharmacology.
[31] Herbert S. Rosenkranz,et al. Expert‐system comparison of structural determinants of chemical toxicity to environmental bacteria , 1994 .
[32] Miss A.O. Penney. (b) , 1974, The New Yale Book of Quotations.
[33] Gerrit Schüürmann,et al. Feed Forward Backpropagation Neural Networks and their Use in Predicting the Acute Toxicity of Chemicals to the Fathead Minnow , 1997 .
[34] N. Metropolis,et al. Equation of State Calculations by Fast Computing Machines , 1953, Resonance.
[35] Yoshiaki Nakagawa,et al. Classical and three-dimensional QSAR for the inhibition of [3H]ponasterone A binding by diacylhydrazine-type ecdysone agonists to insect Sf-9 cells. , 2005, Bioorganic & medicinal chemistry.
[36] C. Hansch,et al. Comparative QSAR analysis of estrogen receptor ligands. , 1999, Chemical reviews.
[37] E. Dodds,et al. A Simple Aromatic (Œstrogenic Agent with an Activity of the Same Order as that of Œstrone , 1937, Nature.
[38] S. Safe,et al. Hydroxylated polychlorinated biphenyls (PCBs) as estrogens and antiestrogens: structure-activity relationships. , 1997, Toxicology and applied pharmacology.
[39] Roger Perkins,et al. QSAR Models for Binding of Estrogenic Compounds to Estrogen Receptor α and β Subtypes. , 1997, Endocrinology.
[40] Mark T. D. Cronin,et al. Predicting Chemical Toxicity and Fate , 2004 .
[41] G. Ankley,et al. Reactivity profiles of ligands of mammalian retinoic acid receptors: A preliminary COREPA analysis , 2002, SAR and QSAR in environmental research.
[42] Zbigniew Dauter,et al. Molecular basis of agonism and antagonism in the oestrogen receptor , 1997, Nature.
[43] Simon K. Kearsley,et al. An alternative method for the alignment of molecular structures: Maximizing electrostatic and steric overlap , 1990 .
[44] C. Waller,et al. Three-dimensional quantitative structure-activity relationships of dioxins and dioxin-like compounds: model validation and Ah receptor characterization. , 1995, Chemical research in toxicology.
[45] J Devillers,et al. QSAR Modeling of Large Heterogeneous Sets of Molecules , 2001, SAR and QSAR in environmental research.
[46] Kimito Funatsu,et al. Multi-way PLS modeling of structure-activity data by incorporating electrostatic and lipophilic potentials on molecular surface , 2003, Comput. Biol. Chem..
[47] O Mekenyan,et al. A computationally based identification algorithm for estrogen receptor ligands: part 1. Predicting hERalpha binding affinity. , 2000, Toxicological sciences : an official journal of the Society of Toxicology.
[48] Han Van De Waterbeemd. Advanced Computer-Assisted Techniques in Drug Discover , 1994 .
[49] Jon G. Wilkes,et al. Use of 13C NMR Spectrometric Data To Produce a Predictive Model of Estrogen Receptor Binding Activity , 2001, J. Chem. Inf. Comput. Sci..
[50] Effect of substituent size and dimensionality on potency of phenolic xenoestrogens evaluated with a recombinant yeast assay , 2000 .
[51] H S Rosenkranz,et al. Development, characterization and application of predictive-toxicology models. , 1999, SAR and QSAR in environmental research.
[52] J. Sumpter,et al. Estrogenicity of alkylphenolic compounds: A 3‐D structure—activity evaluation of gene activation , 2000 .
[53] W. Lawson,et al. Æstrogenic Activity of some Hydrocarbon Derivatives of Ethylene , 1937, Nature.
[54] J Devillers,et al. Multivariate analysis of the first 10 MEIC chemicals. , 1994, SAR and QSAR in environmental research.
[55] Lars Kai Hansen,et al. Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..
[56] O Mekenyan,et al. A reactivity pattern for discrimination of ER agonism and antagonism based on 3-D molecular attributes , 2002, SAR and QSAR in environmental research.
[57] J. Devillers,et al. Prediction of Partition Coefficients (LOG P oct) Using Autocorrelation Descriptors , 1997 .
[58] R. Robinson,et al. Œstrogenic Activity of Certain Synthetic Compounds , 1938, Nature.
[59] V. Laudet,et al. Ligand binding and nuclear receptor evolution , 2000, BioEssays : news and reviews in molecular, cellular and developmental biology.
[60] Igor V. Tetko,et al. Application of Associative Neural Networks for Prediction of Lipophilicity in ALOGPS 2.1 Program , 2002, J. Chem. Inf. Comput. Sci..
[61] D. F. V. Lewis,et al. Molecular modelling of the human glucocorticoid receptor (hGR) ligand-binding domain (LBD) by homology with the human estrogen receptor α (hERα) LBD: quantitative structure–activity relationships within a series of CYP3A4 inducers where induction is mediated via hGR involvement , 2002, The Journal of Steroid Biochemistry and Molecular Biology.
[62] H Hong,et al. An integrated "4-phase" approach for setting endocrine disruption screening priorities--phase I and II predictions of estrogen receptor binding affinity , 2002, SAR and QSAR in environmental research.
[63] Alexander Tropsha,et al. Novel Variable Selection Quantitative Structure-Property Relationship Approach Based on the k-Nearest-Neighbor Principle , 2000, J. Chem. Inf. Comput. Sci..
[64] M Brinn,et al. Neural network classification of mutagens using structural fragment data. , 1993, SAR and QSAR in environmental research.
[65] T. Wiese,et al. Induction of the estrogen specific mitogenic response of MCF-7 cells by selected analogues of estradiol-17 beta: a 3D QSAR study. , 1997, Journal of medicinal chemistry.
[66] C. Porte,et al. Evidence of endocrine disruption in the imposex-affected gastropod Bolinus brandaris. , 1999, Environmental research.
[67] Weida Tong,et al. Decision Forest: Combining the Predictions of Multiple Independent Decision Tree Models , 2003, J. Chem. Inf. Comput. Sci..
[68] A Wenzel,et al. Identification of endocrine-disrupting effects in aquatic vertebrates and invertebrates: report from the European IDEA project. , 2003, Ecotoxicology and environmental safety.
[69] Juhani Ruuskanen,et al. Consensus kNN QSAR: a versatile method for predicting the estrogenic activity of organic compounds in silico. A comparative study with five estrogen receptors and a large, diverse set of ligands. , 2004, Environmental science & technology.
[70] D. Zakarya,et al. QSARs for toxicity of DDT-type analogs using neural network. , 1997, SAR and QSAR in environmental research.
[71] V C Arena,et al. Decision tree SAR models for developmental toxicity based on an FDA/TERIS database , 2003, SAR and QSAR in environmental research.
[72] Gerald T. Ankley,et al. A Computationally-Based Hazard Identification Algorithm That Incorporates Ligand Flexibility. 1. Identification of Potential Androgen Receptor Ligands , 1997 .
[73] J. Devillers,et al. Practical applications of quantitative structure-activity relationships (QSAR) in environmental chemistry and toxicology , 1990 .
[74] V. Laudet,et al. The nuclear receptor superfamily , 2003, Journal of Cell Science.
[75] J. Devillers,et al. Non‐linear mapping for structure‐activity and structure‐property modelling , 1993 .
[76] Jon G. Wilkes,et al. Models of Polychlorinated Dibenzodioxins, Dibenzofurans, and Biphenyls Binding Affinity to the Aryl Hydrocarbon Receptor Developed Using 13C NMR Data , 2001, J. Chem. Inf. Comput. Sci..
[77] Glen Eugene Kellogg,et al. HINT: A new method of empirical hydrophobic field calculation for CoMFA , 1991, J. Comput. Aided Mol. Des..
[78] J. Giesy,et al. Specific binding of hydroxylated polychlorinated biphenyl metabolites and other substances to bovine calf uterine estrogen receptor: structure-binding relationships. , 1999, The Science of the total environment.
[79] Paul Labute,et al. Binary Quantitative Structure-Activity Relationship (QSAR) Analysis of Estrogen Receptor Ligands , 1999, J. Chem. Inf. Comput. Sci..
[80] S. Safe,et al. Polychlorinated biphenyls (PCBs), dibenzo-p-dioxins (PCDDs), and dibenzofurans (PCDFs) as antiestrogens in MCF-7 human breast cancer cells: quantitative structure-activity relationships. , 1993, Toxicology and applied pharmacology.
[81] Anton J. Hopfinger,et al. 4D-QSAR Analysis of a Set of Ecdysteroids and a Comparison to CoMFA Modeling , 2001, J. Chem. Inf. Comput. Sci..
[82] Ranbir Singh,et al. J. Mol. Struct. (Theochem) , 1996 .
[83] K Tuppurainen,et al. Electronic eigenvalue (EEVA): a new QSAR/QSPR descriptor for electronic substituent effects based on molecular orbital energies. A QSAR approach to the Ah receptor binding affinity of polychlorinated biphenyls (PCBs), dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs). , 2000, Chemosphere.
[84] R. Bursi,et al. Application of (quantitative) structure-activity relationships to progestagens: from serendipity to structure-based design. , 2000, European journal of medicinal chemistry.
[85] G. Klopman. Artificial intelligence approach to structure-activity studies. Computer automated structure evaluation of biological activity of organic molecules , 1985 .
[86] D. Douguet,et al. Quantitative structure‐activity relationship studies of RAR α, β, γ retinoid agonists , 1999 .
[87] W S Branham,et al. Quantitative structure-activity relationships (QSARs) for estrogen binding to the estrogen receptor: predictions across species. , 1997, Environmental health perspectives.
[88] H S Rosenkranz,et al. Chemical diversity approach for evaluating mechanistic relatedness among toxicological phenomena. , 1999, SAR and QSAR in environmental research.
[89] H Fang,et al. Quantitative comparisons of in vitro assays for estrogenic activities. , 2000, Environmental health perspectives.
[90] M. Jacobs,et al. In silico tools to aid risk assessment of endocrine disrupting chemicals. , 2004, Toxicology.
[91] Gerald T. Ankley,et al. Quantitative structure‐activity relationships for polychlorinated hydroxybiphenyl estrogen receptor binding affinity: An Assessment of conformer flexibility , 1996 .
[92] Q Xie,et al. Structure-activity relationships for a large diverse set of natural, synthetic, and environmental estrogens. , 2001, Chemical research in toxicology.
[93] Arata Katayama,et al. Endocrine disruptors in the environment (IUPAC Technical Report) , 2003 .
[94] C. Waller,et al. Comparative molecular field analysis of polyhalogenated dibenzo-p-dioxins, dibenzofurans, and biphenyls. , 1992, Journal of medicinal chemistry.
[95] Toshio Fujita,et al. Classical and Three-Dimensional QSAR in Agrochemistry , 1995 .
[96] Weida Tong,et al. Study of 202 natural, synthetic, and environmental chemicals for binding to the androgen receptor. , 2003, Chemical research in toxicology.
[97] Yoshiaki Nakagawa,et al. Binding affinity of nonsteroidal ecdysone agonists against the ecdysone receptor complex determines the strength of their molting hormonal activity. , 2003, European journal of biochemistry.
[98] William J. Welsh,et al. Comparison of Estrogen Receptor α and β Subtypes Based on Comparative Molecular Field Analysis (CoMFA) , 1999 .
[99] Viera Lukacova,et al. Multimode Ligand Binding in Receptor Site Modeling: Implementation in CoMFA , 2003, J. Chem. Inf. Comput. Sci..
[100] G. Habermehl,et al. ReviewPure appl. Chem: Rinehart, K. L., et al. Marine natural products as sources of antiviral, antimicrobial, and antineoplastic Agents. 53, 795 (1981). (K. L. Rinehart, University of Illinois, Urbana, IL 61801, U.S.A.) , 1983 .
[101] P. Jurs,et al. Prediction of acute mammalian toxicity of organophosphorus pesticide compounds from molecular structure. , 1999, SAR and QSAR in environmental research.
[102] H Hong,et al. An in silico ensemble method for lead discovery: decision forest , 2005, SAR and QSAR in environmental research.
[103] Weida Tong,et al. QSAR Models Using a Large Diverse Set of Estrogens , 2001, J. Chem. Inf. Comput. Sci..
[104] Hans Hamersma,et al. Prediction of the Progesterone Receptor Binding of Steroids using a Combination of Genetic Algorithms and Neural Networks , 1996 .
[105] J. Furr,et al. Xenoendocrine disrupters-tiered screening and testing: filling key data gaps. , 2002, Toxicology.
[106] H. Kubinyi,et al. 3D QSAR in drug design. , 2002 .
[107] T. Wayne Schultz,et al. Molecular Quantum Similarity Analysis of Estrogenic Activity , 2003, J. Chem. Inf. Comput. Sci..
[108] Keiji Tanaka,et al. Binding mode of ecdysone agonists to the receptor: comparative modeling and docking studies , 2003, Journal of molecular modeling.
[109] Takahiro Suzuki,et al. Classification of Environmental Estrogens by Physicochemical Properties Using Principal Component Analysis and Hierarchical Cluster Analysis , 2001, J. Chem. Inf. Comput. Sci..
[110] O. Mekenyan,et al. Development and validation of an average mammalian estrogen receptor-based QSAR model , 2002, SAR and QSAR in environmental research.
[111] B. Pavoni,et al. Concentrations of organotin compounds and imposex in the gastropod Hexaplex trunculus from the Lagoon of Venice. , 2004, The Science of the total environment.
[112] G. Ankley,et al. A computationally based identification algorithm for estrogen receptor ligands: part 2. Evaluation of a hERalpha binding affinity model. , 2000, Toxicological sciences : an official journal of the Society of Toxicology.
[113] Toshiyuki Harada,et al. High-throughput screening of ecdysone agonists using a reporter gene assay followed by 3-D QSAR analysis of the molting hormonal activity. , 2006, Bioorganic & medicinal chemistry.
[114] L. Pedersen,et al. PCB and Dioxin Binding to Cytosol Receptors: A Theoretical Model Based on Molecular Parameters , 1984 .
[115] M. Cronin,et al. The Impact of variable selection on the modelling of oestrogenicity , 2005, SAR and QSAR in environmental research.
[116] B. Fan,et al. QSAR study of natural, synthetic and environmental endocrine disrupting compounds for binding to the androgen receptor , 2005, SAR and QSAR in environmental research.
[117] L. Gray,et al. Endocrine Disruptors: Effects on Sex Steroid Hormone Receptors and Sex Development , 1997 .
[118] H. Fang,et al. Comparative molecular field analysis (CoMFA) model using a large diverse set of natural, synthetic and environmental chemicals for binding to the androgen receptor , 2003, SAR and QSAR in environmental research.
[119] Ovanes Mekenyan,et al. Quantitative structure‐activity relationship models for prediction of estrogen receptor binding affinity of structurally diverse chemicals , 2003, Environmental toxicology and chemistry.
[120] E. Dodds,et al. Synthetic Œstrogenic Agents without the Phenanthrene Nucleus , 1936, Nature.
[121] I. Meerts,et al. In vitro estrogenicity of polybrominated diphenyl ethers, hydroxylated PDBEs, and polybrominated bisphenol A compounds. , 2001, Environmental health perspectives.
[122] J. Devillers. Genetic algorithms in molecular modeling , 1996 .
[123] J. Sumpter,et al. Structural Features of Alkylphenolic Chemicals Associated with Estrogenic Activity* , 1997, The Journal of Biological Chemistry.
[124] J. Abecassis,et al. Effect of triphenylacrylonitrile derivatives on estradiol-receptor binding and on human breast cancer cell growth. , 1989, Journal of medicinal chemistry.
[125] R. Kavlock,et al. Drug Toxicity in Embryonic Development I , 1997, Handbook of Experimental Pharmacology.
[126] J Devillers,et al. Structure-toxicity modeling of pesticides to honey bees , 2002, SAR and QSAR in environmental research.
[127] Deborah A. Loughney,et al. A comparison of progestin and androgen receptor binding using the CoMFA technique , 1992, J. Comput. Aided Mol. Des..
[128] J. B. Holt,et al. Observed abnormalities in mandibles of nestling bald eagles Haliaeetus leucocephalus , 1994, Bulletin of environmental contamination and toxicology.
[129] W. A. Toscano,et al. QSAR Models of the in vitro Estrogen Activity of Bisphenol A Analogs , 2003 .
[130] James Devillers,et al. Neural Networks in QSAR and Drug Design , 1996 .
[131] Hugo Kubinyi,et al. 3D QSAR in drug design : theory, methods and applications , 2000 .
[132] Robert E. Hormann,et al. An extensive ecdysteroid CoMFA , 1999, J. Comput. Aided Mol. Des..
[133] Mikko Kolehmainen,et al. Structure-based classification of active and inactive estrogenic compounds by decision tree, LVQ and kNN methods. , 2006, Chemosphere.
[134] Wolfgang Sippl,et al. Receptor-based 3D QSAR analysis of estrogen receptor ligands – merging the accuracy of receptor-based alignments with the computational efficiency of ligand-based methods , 2000, J. Comput. Aided Mol. Des..
[135] G. Veith,et al. A QSAR evaluation of Ah receptor binding of halogenated aromatic xenobiotics. , 1996, Environmental health perspectives.
[136] A QSPR Study of Sweetness Potency Using the CODESSA Program , 2002 .
[137] Robert E. Hormann,et al. Superimposition evaluation of ecdysteroid agonist chemotypes through multidimensional QSAR , 2003, J. Comput. Aided Mol. Des..
[138] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[139] H S Rosenkranz,et al. Structure–Activity Approach to the Identification of Environmental Estrogens: The MCASE Approach , 2004, SAR and QSAR in environmental research.
[140] D. Rogers,et al. Some Theory and Examples of Genetic Function Approximation with Comparison to Evolutionary Techniques , 1996 .
[141] H. Gardner,et al. Chronic toxicity of chloroform to Japanese medaka fish. , 2000, Environmental health perspectives.
[142] S. Kharb. Toxicology , 1936 .
[143] D. Fry. Reproductive effects in birds exposed to pesticides and industrial chemicals. , 1995, Environmental health perspectives.
[144] J Devillers,et al. Heuristic potency of the minimum spanning tree (MST) method in toxicology. , 1989, Ecotoxicology and environmental safety.
[145] X Gironés,et al. Using molecular quantum similarity measures as descriptors in quantitative structure-toxicity relationships. , 1999, SAR and QSAR in environmental research.
[146] Weida Tong,et al. Prediction of estrogen receptor binding for 58,000 chemicals using an integrated system of a tree-based model with structural alerts. , 2001, Environmental health perspectives.
[147] James Devillers,et al. PREDICTION OF TOXICITY OF ORGANOPHOSPHORUS INSECTICIDES AGAINST THE MIDGE, CHIRONOMUS RIPARIUS, VIA A QSAR NEURAL NETWORK MODEL INTEGRATING ENVIRONMENTAL VARIABLES , 2000 .
[148] Takako Aizawa,et al. Quantitative structure-activity relationships for estrogen receptor binding affinity of phenolic chemicals. , 2003, Water research.
[149] D. Fry,et al. DDT-induced feminization of gull embryos. , 1981, Science.